Design and Implementation of Telemedicine based on Java Media Framework

AbstractAccording to analyze the importance and problem of telemedicine in this paper, a telemedicine system based on JMF is proposed to design and implement capturing, compression, storage, transmission, reception and play of a medical audio and video. The telemedicine system can solve existing problems that medical information is not shared, platform-dependent is high, software is incompatibilities and so on. Experimental data prove that the system has low hardware cost, and is easy to transmission and storage, and is portable and powerful

Visual Simulation of Missile Attacking Battleplane Based on Vega

A visual simulation system of fragment warhead missile attacking F-16C ''Falcon'' battleplane based on simulation and virtual reality technology is put forward. Firstly, the overall design of visual simulation of missile attacking F16 battleplane is implemented, and all functions of each module are demonstrated in detailed. Then 3D models in virtual battle field are optimized by level of detail, texture mapping, billboard and instance technology. Finally, Vega scene driving program is developed, and the implementation of special effect, view transform, preview and collision detect are emphasized. The result of simulation provides reference for damage assessment of missile attacking F16 battleplane. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.394

Spatial deformable transformer for 3D point cloud registration

Abstract Deformable attention only focuses on a small group of key sample-points around the reference point and make itself be able to capture dynamically the local features of input feature map without considering the size of the feature map. Its introduction into point cloud registration will be quicker and easier to extract local geometric features from point cloud than attention. Therefore, we propose a point cloud registration method based on Spatial Deformable Transformer (SDT). SDT consists of a deformable self-attention module and a cross-attention module where the deformable self-attention module is used to enhance local geometric feature representation and the cross-attention module is employed to enhance feature discriminative capability of spatial correspondences. The experimental results show that compared to state-of-the-art registration methods, SDT has a better matching recall, inlier ratio, and registration recall on 3DMatch and 3DLoMatch scene, and has a better generalization ability and time efficiency on ModelNet40 and ModelLoNet40 scene

Metabolic Brain Network Analysis of FDG-PET in Alzheimer’s Disease Using Kernel-Based Persistent Features

Recent research of persistent homology in algebraic topology has shown that the altered network organization of human brain provides a promising indicator of many neuropsychiatric disorders and neurodegenerative diseases. However, the current slope-based approach may not accurately characterize changes of persistent features over graph filtration because such curves are not strictly linear. Moreover, our previous integrated persistent feature (IPF) works well on an rs-fMRI cohort while it has not yet been studied on metabolic brain networks. To address these issues, we propose a novel univariate network measurement, kernel-based IPF (KBI), based on the prior IPF, to quantify the difference between IPF curves. In our experiments, we apply the KBI index to study fluorodeoxyglucose positron emission tomography (FDG-PET) imaging data from 140 subjects with Alzheimer’s disease (AD), 280 subjects with mild cognitive impairment (MCI), and 280 healthy normal controls (NC). The results show the disruption of network integration in the progress of AD. Compared to previous persistent homology-based measures, as well as other standard graph-based measures that characterize small-world organization and modular structure, our proposed network index KBI possesses more significant group difference and better classification performance, suggesting that it may be used as an effective preclinical AD imaging biomarker

Deep cross-modal discriminant adversarial learning for zero-shot sketch-based image retrieval

Zero-shot sketch-based image retrieval (ZS-SBIR) is an extension of sketch-based image retrieval (SBIR) that aims to search relevant images with query sketches of the unseen categories. Most previous methods focus more on preserving semantic knowledge and improving domain alignment performance, but neglect to capture the correlation between inter-modal features, resulting in unsatisfactory performance. Hence, a sketch-image cross-modal retrieval framework is proposed to maximize the sketch-image correlation. For this framework, we develop a discriminant adversarial learning method that incorporates intra-modal discrimination, inter-modal consistency, and inter-modal correlation into a deep learning network for common feature representation learning. Specifically, sketch and image features are first projected into a shared feature subspace to achieve modality-invariance. Subsequently, we adopt a category label predictor to achieve intra-modal discrimination, use adversarial learning to confuse modal information for inter-modal consistency, and introduce correlation learning to maximize inter-modal correlation. Finally, the trained deep learning model is used to test unseen categories. Extensive experiments conducted on three zero-shot datasets show that this method outperforms state-of-the-art methods. For retrieval accuracy of unseen categories, this method exceeds the state-of-the-art methods by approximately 0.6% on the RSketch dataset, 5% on the Sketchy dataset, and 7% on the TU-Berlin dataset. We also conduct experiments on the dataset of image-based 3D model scene retrieval, the proposed method significantly outperforms the state-of-the-art approaches in all standard metrics